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Tackling Receptor Flexibility in
Computer-Aided Drug Design
Rommie Amaro . NBCR Mini-Symposium . August 4, 2008
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Computer-aided drug design
Van Drie, J., J. Comp. Aid. Mol. Des., 21: 591-601 (2007)
Challenges:
solvation effects, entropy,
rigorous thermodynamics
prediction of
lead/candidate
pharmocokinetic properties
networks /
polypharmacology
receptor flexibility
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Tackling receptor flexibility:
relaxed complex scheme
Amaro, Baron, and McCammon, J. Comp. Aid. Mol. Des..,in press (2008)
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Developing new antivirals against
avian influenza Biological introduction
Investigating the dynamics and flexibility of N1
(molecular dynamics)
Extracting meaningful information and reducing
redundancy (clustering analysis)
Finding new druggable hot spots (computational solvent
mapping)
Identifying new drugs for experimental testing (virtualscreening)
Summary & future work
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Influenza virus
~100 nm diameter
Neuraminidase
(9 subtypes)
Hemaggluttinin
(16 subtypes)
M2 ion channel
Host-derived lipid
envelope
8 RNA segments
No proof reading during
replication - highly variable
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Influenza
Epidemics are normal, seasonal influenza outbreaks
est. 300,000-500,000 people die each year due toepidemic influenza
deaths highest among > 65 yrs old, children < 2 yrs,immunocompromised
Pandemics are rare events that occur every 10-50 years.
In the last 400 years, at least 31 pandemics have beenrecorded
Circulate around the globe in successive waves
With global travel, est. a new pandemic would reach almost allcorners of the earth within 3-6 months, an estimated 2 billionof the worlds 6.5 billion people will be infected
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Origin of pandemic viruses
Clercq, Nat Rev. Drug Disc.,5: 1015-1025 (2006)
40 milliondeaths 1-1.5 million
deaths
0.75 - 1 million
deaths
antigenicdrift antigenic
shift
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It is especially virulent (~ 50% mortality rate) & being spread by migratory birds
Bird to mammal, bird to human transmission
Like other influenza viruses, it continues to evolve.
H5N1 influenza cases 2003-2008
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Points of intervention in the viral replication
cycle
Clercq, Nat Rev. Drug Disc.,5: 1015-1025 (2006)
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Group 1 and 2 neuraminidases
Russell et al, Nature, 443: 45-49 (2006).
9 neuraminidase (NA) strains:
GroupGroup 1 ?1 ?
2 phylogenetically distinct groups:
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Group 1 and 2 neuraminidases
Russell et al, Nature, 443: 45-49 (2006).
2 phylogenetically distinct groups:
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Goals
To develop a more effective, orally-available drug
against N1
Methodological goal: to develop optimized scheme forreceptor flexibility in inhibitor discovery process
Use the structural information from MD as a predictive
guide and to expand the receptor ensemble
Improve final ranking of compounds and account for
induced-fit effects, as part of improved drug discovery
scheme
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Molecular dynamics to probe structure
& dynamics
Van der Waals& electrostatics
t t. . .
U
!
R( ) = kbond r ! ro( )2
+ k" "!"o( )2
+ kdihed 1+ cos n#+ $( )%& '( + 4)ij*ij
rij
+
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.
/0
12
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.
/0
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Classical dynamics
at 300K : !
Fi= ma = m
i
d2!
ri
dt2= !
!
"U!
R( )
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Molecular dynamics simulations
2HTY (open loop, apo) 2HU0 (open loop, holo)
N1 tetramer, (ligands), ions
Explicit solvent, 150mM NaCl
112,457 atoms
NAMD2 on supercomputers
5 ns/day
40 ns for the tetramer
(eq. of 160 ns of monomer)
Amaro, R. E., Minh, D.D.L., Cheng, L.S., Lindstrom, Jr., W., Olson, A.J., Lin, J.-H., Li, W.W., and McCammon, J.A., JACS, 129: 7764 7765 (2007).
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Remarkable loop flexibility
Molecular dynamics allows
sampling of receptor side
chains and larger local
motions (e.g. loop sampling)
Can account for induced
effects of particular ligand
(e.g. Tamiflu)
Changes in ligand binding
site can be exploited anddesigned around
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150-loop dynamics
open, closed
Amaro, R. E., Minh, D.D.L., Cheng, L.S., Lindstrom, Jr., W., Olson, A.J., Lin, J.-H., Li, W.W., and McCammon, J.A., JACS, 129: 7764 7765 (2007).
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Implications for Antiviral Drug Design
430-loop and 150-loop
very flexible
Structural reorganization
reveals new pockettopography
Goal:
To use these new
structural insights fordrug discovery/design
efforts
Amaro, R. E., Minh, D.D.L., Cheng, L.S., Lindstrom, Jr., W., Olson, A.J., Lin, J.-H., Li, W.W., and McCammon, J.A., JACS, 129: 7764 7765 (2007).
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Clustering distills essential information
Extracted snapshots from 4 chains explicit 40 nssimulations (160 ns for both apo & holo)
Alignment based on Catoms
Then computed RMSD distance matrix usingsubset of 62 residues (sidechains included) liningthe binding pocket
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Computational solvent mapping
Assesses druggability of receptor surfaces using complementary physics-based approach
14 organic probes to flood receptor surface
Probes clustered and ranked by interaction energy with surface
Hot spots indicate areas of high functional group affinity
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Hot spots predict areas of affinity
Landon, M., Amaro, R.E., Baron, R., Ngan, C.H., Ozonoff, D., McCammon, J.A., and Vadja, S., Chemical Biology & Drug Design (2008).
Structures revealed by
MD have new high
affinity areas for ligands,
ligand-extensions to bind
These hot spots vary in
size, number, and moiety
Indicates which
residues in new areas
may be important tooptimize against
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Discovering new inhibitors:
virtual screen with molecular dynamics
Typical virtual screens use only one crystal structure
Virtual screen of 3 most dominant MD cluster
representative structures & crystal structures
Rapid docking with AutoDock
Against the NCI diversity set ~ 2000 compounds
Top candidates filtered for druglikeness & clustering
Identified 27 novel putative inhibitors, half of which would
not have been found based on crystal structures alone
Ordering of known sialic acid analog inhibitors is correct
(positive controls: Tamiflu, Relenza, DANA)
Cheng, L.S., Amaro, R.E., Xu, D., Li, W.W., Arzberger, P.A., and McCammon, J.A., Journal of Medicinal Chemistry, in press (2008).
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Ensemble-based virtual screening
Closed 150-loop Open 150-loop
Including full receptor flexibility opens new areas for ligand binding
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Potential cross-cavity binders
Several compounds are
predicted to bind 2 or
more cavities
May provide addtl
selectivity for N1
Many ligands predictedto dock to the CS-map
hot spots
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Rescoring can be important!
African trypanosomiasis
RNA editing ligase required for survival of parasite
Rescoring of top compounds provided important enrichment of
recommended set
Limited experimental resources, best inhibitors would not have been
tested without rescoring
Amaro, R., Schnaufer, A., Interthal, H., Hol, W., Stuart, K., and McCammon, J.A., submitted (2008)
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Future methodological work:
relaxed complex scheme
Amaro, Baron, and McCammon, J. Comp. Aid. Mol. Des..,in press (2008)
Developing a
workflow tool
using Vision
Needs to be
flexible so new
modules can be
easily added
Developingcyberinfrastructure
to launch jobs,
deal with &
manipulate data
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Avian Flu Grid: an international
collaborative effort
SDSC node
(in USA )
AIST cluster
(in Japan)
gfsd
GRAM
Job submission
(globusrun)
mpirun
File I/O
Gfarm filesystem
USM node
(in Malaysia)
Users
Users
Data, program
- Computational server
- Storage server
PRAGMA testbed
Collaboration
AIST , ASGC, CNIC, CUHK, GUCAS,
IOIT-HCM, LZU, MIMOS, NECTEC,
NGO, SDSC, ThaiGrid , UZH, VPAC
32 institutions in 16
countries across the
Pacific Rim and USA
N1 project science
driver for technology
development
Developing computational environment
(infrastructure) and scientific
applications
Portal for datasharing
http://www.pragma-grid.net/
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Training and Outreach
H5N1 projects serve as training projects for undergraduate
students through PRIME and high schoolers through the
Pinhead Institute
Thursday & Friday Track III sessions will teach YOU how to
set up an MD simulation, perform analysis, submit a virtual
screen and perform a relaxed complex scheme rescoring
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Molecular Graphics Lab
Professor Andy McCammon
The McCammon Group
Acknowledgements
The SAFI & Avian Flu Grid Teams
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H5N1: why so deadly?
H5N1 seems to inducehypercytokinemia, a.k.a. cytokine storm
Overreaction of the innate immune
system, which is highly complex in its
interactions with other signaling
molecules, is suspected to play a role in
the virulence
Preference for sialic acid receptors in
the lower respitory tract (as opposed to
upper) = delayed side effects (sneezing,
coughing, etc) = longer virus incubation
period, so when presents itself, higher
viral load, tougher on the body
Onset of symptoms to death: 9 days
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Biomolecular simulations & the future of
computer-aided drug design
Increased computing power, entering the petascale era
Simulations of hundreds of ns already possible, microseconds
soon to follow
Highly optimized parallel code allow building of complexity(bigger systems), without sacrificing speed
Enabling of grid-based technologies offer alternative computing
platforms for docking or other small-processor request jobs
As compute power grows, so will the scope and level of CADDmodeling
Good predictions cut time to positive experiment, assist in
understanding mechanism of action, drive discoveries!
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Generalized Born MD
- Projects with Xiaolin Cheng & Ivaylo Ivanov (McCammon group)
- GB: Represents the solvent implicitly as continuum with the dielectric
properties of water, and includes the charge screening effects of salt:
N1-apo, closed loop | N9-apo, closed loop
N1-tamiflu, closed loop | N9-tamiflu, closed loop
N1-tamiflu, open loop | N1-apo, open loopTetramer N1 system = HUGE! (20K+ atoms)...
- 16 ns for each system, Amber igb version 5, monomer only, with
Ambers fast pmemd MD engine (~5500 atoms: big for GB)
- On new NCSA Abe machine, scales to 256 - 512 procs, ~ 8 ns/day
-Comparative dynamics analysis between N1 vs. N9, tamiflu bound and
apo systems, open & closed loops possibly sample more open/closed
loop transitions
Manuscript in preparation may use snapshots for CADD work
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GB-MD Preliminary Results
open, closed
N1-apo-closed
N1-tami-closed
N1-apo-openN1-tami-open
N9-apo-closed N9-tami-closed
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GB-MD Preliminary Results
N9-closedN1-closed N1-open
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Grid Maps
Fast energy evaluation is achieved by precalculating atomicaffinity potentials (grid maps), one for each atom type in the
ligand
Calculated by autogrid & a .gpf file
Affinity grid: each point stores the potential energy of a
probe atom due to all atoms in the macromolecule Also makes electrostatic maps
Define receptor atom types,
ligand atom types
npts 60 60 60
spacing 0.375
gridcenter 1.602 18.973 4.55
AutoDock Users Guide, v3.0.5, Morris et al.
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AutoDock4 force field
!G = VboundL"L
"VunboundL"L( )+ VboundP
"P"Vunbound
P"P( )+ VboundP"L"Vunbound
P"L+ !Sconf( )
Intramolecular energies Intermolecular energies
!Sconf =WconfNtors Loss of torsional entropy upon binding
Huey, Morris, Olson & Goodsell, J. Comp. Chem, A Semi-empirical Free Energy
Force Field with Charge-based Desolvation, preprint (2006).
0 0
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V=Wvdw Aij
rij12
! Bijrij6
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ij
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iqj
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rij2 2)2( )
i, j*
i, j*
i, j*
i, j*
Normal Lennard-Jones potential describing
dispersion/repulsion interactionsParameters A and B taken from the Amber forcefield.
Semi-empirical: combines traditional MM force fieldswith empirical weights and an empirical approach for
entropic contributions
AutoDock force field
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Semi-empirical: combines traditional MM force fields withempirical weights and an empirical approach for entropic
contributions
V=Wvdw Aij
rij12
! Bijrij6
"#$ %
&'+Whbond E(t) C
ij
rij12
! Dijrij10
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Directional H-bond term based on a 10/12 potential.
C and D give a maximal well depth of 5 kcal/mol at 1.9 for OH andNH, and a depth of 1 kcal/mol at 2.5 for SH.
Directionality of the hydrogen bond interaction E(t) is dependent on the anglet away from ideal bonding geometry. Note that the directionality is only withrespect to the receptor:
AutoDock force field
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Semi-empirical: combines traditional MM force fields withempirical weights and an empirical approach for entropic
contributions
V=Wvdw Aij
rij12 ! B
ij
rij6"
#$ %
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ij
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Electrostatics described by a screened coulombic potential
AutoDock force field
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AutoDock4
Fast energy evaluation is achieved byprecalculating atomic affinity potentials
Affinity grids: each point stores the potentialenergy of a probe atom due to all atoms inthe macromolecule
Each atom type in ligand gets a map
Full ligand flexibility around all torsions Lamarckian genetic algorithm
Very efficient global search
AutoDock Users Guide, v3.0.5, Morris et al.
Based on comprehensive thermodynamic model that allows incorporation of
intramolecular energies into the predicted free energy of binding
Charge-based method for evaluation of desolvation for typical set of atomtypes2
Calibrated against 188 diverse protein ligand complexes2Stouten et al., Molecular Simulation, 10: 97-120 (1993).
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AutoDock4 force field
!Sconf =WconfNtors Loss of torsional entropy upon binding
Huey, Morris, Olson & Goodsell, J. Comp. Chem, A Semi-empirical Free Energy Force Field with Charge-based Desolvation, preprint (2006).
V=WvdwAij
rij12
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*
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"VunboundP"L
+ !Sconf( )
Intramolecular energies Intermolecular energies
0 0